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<b>OMOLAND-CA : An Agent-based Modeling of  Climate Change Adaptation
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<b><span style="font-size: 10.0pt; mso-bidi-font-size: 12.0pt;">(Version 1.0)</span></b></div>

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<b><u>Developers:</u></b></div>

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<b>Atesmachew Hailegiorgis, Claudio Cioffi-Revilla, and Andrew Crooks</b></div>

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<b><span style="font-size: 10.0pt;"><a href="mailto:ahailegi@gmu.edu">ahailegi@gmu.edu</a> | <a href="mailto:ccioffi@gmu.edu">ccioffi@gmu.edu</a> | <a href="mailto:acrooks2@gmu.edu">acrooks2@gmu.edu</a></span></b></div>

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<b>OVERVIEW:</b>
The purpose of the model (which we call OMOLAND-CA) is to investigate the adaptive capacity of rural households in the South Omo zone of Ethiopia (i.e., the study area) with respect to variation in climate, socioeconomic factors, and land-use at the local level. The model has been designed for scientists, lands managers, and policymakers particularly those interested in understanding the coupled natural and human systems, and evaluating the impacts of climate change on the rural system and how rural households’ choice of livelihood affect their adaptive capacity.
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<b>DETAILS:</b>
The South Omo Zone of Ethiopia is conceptualized as a coupled human and natural system (CHANS) comprising a set of interrelated components interacting at different spatial and temporal scales. Within the model, the main agents represent individual households that live in a subsistence agricultural system (herding and/or farming) within the study area. The socio-cognitive behavior of households toward climate change and resource flows that allow them to diversify their production strategy under different climatic situations is designed based on the Model of Private Proactive Adaptation to Climate Change (MPPACC) framework proposed by (Grothmann & Patt, 2005). The environment represents the biophysical environment. It has a spatial extent of 146.7 by 224.7 kilometers (i.e., the South Omo Zone) with a spatial resolution of 100 by 100 meters (i.e., 1 Hectare). Climate is represented primarily by rainfall in terms of precipitation and includes patterns from normal to extreme events (e.g., drought). The temporal resolution of the model is modeled as discrete time step in which each model step represents one day.   
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<b>OPERATION:</b>
 The controls tabs above support the <em>Display</em> of various graphing schemes, for individual agents (dots) "double click" <em>Inspector</em> queries, and <em>Model</em> simulation setup.  Each pixel represents an area 100 by 100 meters in size.
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<b>ACKNOWLEDGMENTS:</b>
This work was supported by the Center for Social Complexity at George Mason University and NSF-DDRI (Doctoral Dissertation Research Initiative) grant no. 112348. The opinions, findings, and conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the sponsors.
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